Category: Technologies

Product update: INOS used in Pre-Launch Testing for Africell Angola Network

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As part of the recent successful commercial launch of Africell Angola, the Digis Squared AI-tool INOS, powered by Intel® Xeon® Scalable Processors, was used as a key part of the network cluster testing and acceptance process. Digis Squared’s Key Account Manager for Africell, Ahmed Ma’moon, shares more.

“Digis Squared manages the entire end to end Managed Services for the new Africell Angola mobile network*. Ahead of the commercial launch on 7th April, and working closely alongside our partners, we ensured that the network was tested robustly before launch.”  [*Read more about that, here.]

“Whilst the capabilities of our vendor-agnostic tool, INOS, are extensive, in the pre-launch phase of the Africell Angola project, its major role was in field optimization following the SSV (Single Site Verification) and sites acceptance phase. The team used INOS devices out in the field in vehicles to collect network performance data for all live sites. We were able to optimise our resources too – thanks to the ability to remotely update scripts, we didn’t need to send engineers out into the field; the INOS kit can be driven to a specific location and along a predefined route by anyone, and the data is automatically uploaded into the cloud immediately.”

What is a mobile network cluster?

Mobile network coverage is often drawn as a honeycomb-like pattern of neatly meshing hexagons.

In reality, the coverage is not neatly tessellated hexagons, but very irregular shapes, due to the landscape, buildings and other features, and coverage from adjacent cells may overlap, or there may be some thin gaps. Mobile network planning engineers allocate different frequency bands (also called channels) to neighbouring cells – this helps to minimise interference even when coverage areas overlap slightly. The group of cells on different bands is known as a cluster.

INOS for Cluster Optimisation

Ahmed continues, “Working from Digis Squared’s offices, our engineers were able to control and update scripts remotely, push revised routes to drivers, and review data live in the cloud during the tests. During the pre-launch phase we ran field measurements using INOS for Luanda province clusters and sub-clusters, undertook the analysis to identify coverage issues, implemented optimisation changes live on the network, and then re-tested and benchmarked the results against the initial data.”

Above: Example INOS dashboard for field measurements used for analysis and optimization

“INOS’ AI-capabilities ensure that analysis of vast amounts of data is completed very rapidly – within 15 minutes of uploading data – so we were able to assess the results, implement fixes and re-run the tests very swiftly.”

Above: Sample coverage analysis algorithms in INOS

Comprehensive pre-launch testing to optimise for post-launch excellence

“INOS was a vital tool for us in the pre-launch field optimization activities for Africell Angola, to ensure best network coverage and performance, and excellent user experience after launch. INOS helped to speed up the field optimization process for all Luanda clusters, and complete the work in advance of the scheduled launch date.”

INOS & Intel

“INOS was a vital tool for us in the pre-launch field optimization activities for Africell Angola, to ensure best network coverage and performance, and excellent user experience after launch. INOS helped to speed up the field optimization process for all Luanda clusters, and complete the work in advance of the scheduled launch date.”

INOS can be implemented as a public or private cloud, or on-premise solution, and is also available as a “Radio Testing as-a-service” model. Its extensive AI-analysis and remote OTA capabilities ensure speedy and accurate assessment of all aspects of network testing: SSV, in-building and drive testing, network optimisation and competitor benchmarking, across all vendors, network capabilities and technologies, including 5G, private networks and OpenRAN.

INOS is built with compute resources powered by Intel® Xeon® Scalable Processors.

In conversation with Ahmed Ma’moon, Digis Squared’s Key Account Manager for Africell Group.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

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Digis Squared, independent telecoms expertise.

Image credits: all images, Digis Squared

Digis Squared & Private Networks

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Yasser Elsabrouty, Digis Squared Chief Business Officer and Co-Founder, shares updates on the Private Network approach Digis Squared is undertaking for clients, and developments in the pipeline. But first, what is a Private Network?

 

What is a Private Network?

Built specifically for individual businesses or organisations, these dedicated LTE or 5G networks have typically been envisaged for mission-critical or highly secure environments. Increasingly however, businesses are deploying them to ensure robust coverage and capacity, reinforce intellectual property protection, and deliver commercial independence from the major network operators and CSPs within their physical business campus-environment.

Private Networks can be deployed in many different shapes and sizes, using various mix and match combinations of spectrum, applications and other factors.

Deployment

  • Dedicated, on-premise networks for both radio access network and core network
  • Hybrid use of some public mobile components plus dedicated on-premise components

Spectrum

  • Industrial: in some countries, regulators allocate specific licensed spectrum (Germany and Japan for example)
  • Shared: regulators allocate spectrum which is shared by multiple stakeholders, under license
  • Public: MNOs or CSPs lease part of their licensed spectrum to an enterprise for a fee
  • Unlicensed spectrum: assigned by the regulator, non-exclusive, free-to-use

 

 

Digis Squared & Private Networks

Digis Squared provides end-to-end System Integration services across multiple technologies including RAN, 4G/5G Core, Security, Messaging and Cloud, covering design, installation, testing, managed services operations.

Yasser Elsabrouty, Digis Squared Chief Business Officer and Co-Founder shares insights into the Private Network activity of the team.

“At Digis Squared, we are providing end to end Consultancy and System Integration services to build private networks to meet our customers’ needs. We work very flexibly with our clients – some know exactly what they need, and will ask Digis Squared to manage the System Integration, deployment and operational aspects of their pre-defined project. Others ask us to define all end to end elements: the deployment model, Spectrum, Radio details, Core network, orchestration and applications. Our teams are able to work with considerable flexibility, and according to the customer’s requirements and use cases to ensure they have and optimised Private Network which meets their needs.”

“We also offer predefined mix and match ‘off the shelf’ models that can be used as a starting point, and then adapt and deploy for the specific, customised and bespoke needs of each customer. We can take into account their specific requirements, including the size of the facility, devices deployed, which machines need to communicate with which departments, and other considerations. Some clients are looking at Private Networks to resolve specific coverage issues, or explore latency management for time-sensitive networks (TSNs). Whatever the scope of the Private Network, project our teams are enabling customers to scale up their businesses, serving more customers and satisfying the ever-increasing demand for private networks.ֿ”

“Using this mix-and-match method, we are developing profiles for different kinds of customers that can easily be implemented as needed, depending on size, area, number of sensors and cameras, or any other parameter. This approach will save time in deploying systems for customers, serving more customers while ensuring top-quality, optimised installations.”

 

 

If you would like to arrange a dedicated time to talk with the team, please get in touch, sales@digissquared.com

 

 

Digis Squared, independent telecoms expertise.

We transform telecom networks, deploy new technologies, and manage vendors, for operators, service providers and regulators.

Apply our expertise, automated AI-led tools and processes to transform your technical and commercial capabilities. We work with agility, deep experience, and our in-house cognitive tools to optimise and manage multi-vendor networks across all technologies.

 

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Image credit: Sarah Doffman (Birmingham)

Product update: cognitive tools “OpenRAN Ready”

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Ahead of MWC22, Digis Squared’s CTO, Abdelrahman Fady, shares insights into ongoing development work on the Digis Squared suite of cognitive network testing and optimisation solutions, and declares tools “OpenRAN Ready”.

“The development teams in our Technology Centres in Cairo and London have been very busy enhancing the existing suite of cognitive tools to ensure that they are “OpenRAN Ready”. In advance of MWC next week, I’m really happy to share some of the key updates underway,” stated Abdelrahman.

Digis-One: technology and vendor agnostic Unified Fault Management

  • Can now connect with the big four legacy vendors and main OpenRAN vendors
  • Unifying all alarms from across all network systems and vendors into a single screen
  • OpenRAN solutions seamlessly integrated to a single view on one screen

iPM: intelligent technology and vendor agnostic network performance management platform

  • Able to connect to legacy vendors in addition to main OpenRAN vendors, and integrate their different performance files into a single unified database
  • Unify and visualize all these KPIs into a single coherent view on one screen, and represent them geographically
  • Single touch comparison between legacy vendors and OpenRAN vendors performance

INOS: technology and vendor agnostic intelligent network field testing and optimisation solution

  • New INOS OpenRAN testing and analytics module launched to easily identify gaps and differences in performance, L3 & L2 messages content and formats, network throughputs, and measure quality between OpenRAN and Legacy RAN sites
  • Forecasting of vulnerable areas after OpenRAN deployment
  • Automated acceptance report for new Open RAN sites

“If you want to learn about our new “OpenRAN Ready” cognitive network optimisation solution capabilities,” said Abdelrahman, “I will be pleased to meet with you in Barcelona next week, please get in touch!”

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

Addendum, 22 February 2022

LinkedIn ◦  Following Digis Squared’s earlier “OpenRAN Ready” solution announcement, Hazem Amiry, Regional Sales & Business Development Manager, shared that, “We’ve been working for some time now on an OpenRAN PoC with a very large operator in the Middle East, as the lead contractor on a project with a multi-awarding winning software-enabled OpenRAN solution provider. This project is enabling the team to learn first-hand the benefits of OpenRAN deployment, and ensure we are able to fully optimise our suite of cognitive tools to real-life, complex deployment issues efficiently.”

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Digis Squared, independent telecoms expertise.

Product update: “Radio Testing as a Service” – successful cloud-based INOS installation in Intel Lab

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Digis Squared’s team complete INOS migration from local on-premises deployment to first cloud-based installation enabling “Radio Testing as a Service”, with Intel® Xeon® Gold 6338N Processor, in the Intel Lab.

Thanks to membership of Intel Network Builders, work undertaken in the Intel Lab has enabled the Digis Squared team to run INOS over the Intel® Xeon® Gold 6338N processor, in the first cloud-based installation of INOS. This work is the first step in our assessment of INOS as a cloud-based solution with Intel processors. Further work is planned with the Intel Lab team assessing other enhanced processors and benchmarking performance enhancement.

Yasser Elsabrouty, Digis Squared Chief Business Officer and Co-Founder said, “Thanks to Intel Network Builders and membership of Intel Winners Circle, INOS is now providing Radio Testing automation over the cloud, enabling “Radio Testing as a Service” over private or public cloud.  The cognitive testing tool can seamlessly manage large amounts of data in a multi-tenant environment, providing full automation and real-time reporting.”

“Delivering INOS Testing as a Service over the cloud will increase efficiency, convenience and scalability, delivering the instant capability to run thousands of radio network tests from anywhere, anytime, in combination with smart automation, real-time reports and KPI deviation alerts. Digis Squared’s cognitive INOS tool just became a whole lot smarter!”

Intel® Xeon® Gold 6338N processor

  • 3rd Generation Intel® Xeon® Scalable Processors (formerly “Ice Lake”)
  • 10nm technology, 32 cores, 64 threads, 3.6GHx max turbo frequency, full specification.

Benefits & observations

Running 25 INOS Radio Field Tests in the Intel Lab, the following enhancements were measured, and benefits observed,

1. Increased cores & threads

  • The Intel® Xeon® Gold 6338N processor enabled Digis Squared to setup 2 or more parallel INOS containers serving two (o more) different customer accounts. In the field, this extra capability enabled by the Intel® Xeon® Gold 6338N processor would mean that,
    • More copies of INOS modules can run together in parallel, providing higher processing capability
    • Lower response time and faster handling for APIs and web requests
    • Duplicating INOS running modules presents high availability
  • When assessing response time across all 25 tests, the results show that the Intel® Xeon® Gold 6338N easily handles the volume of data as data payload increases x2.5 over the 25 tests.

2. Max Turbo Frequency: the INOS platform receives high traffic bursts periodically, due to the nature of telecoms. The increased max turbo frequency of the Intel Xeon processor empowers INOS to handle these bursts without any probability of outage.

3. Intel® Turbo Boost Technology 2.0 Frequency: Increases the capability of INOS to receive big sudden bursts of requests, keeping stable progress and high performance (i.e. no delay on data retrieval, no delay on rendering data to maps and tables, and reduced time to prepare reports.)

4. Number of UPI links: INOS consumes a huge volume of processor capability and RAM. To optimise INOS performance, we are looking not just for capacity of the processor, but also how this processor chip interconnects with the rest of the system components. The Intel® Xeon® Gold 6338N presents better integration with various I/O devices reflecting in INOS performance, especially when handling large bursts of input data files.

5. Max memory size: For INOS, more memory means more concurrent users, more software threads running in parallel, and an increase in the number of docker containers running simultaneously. The increased max memory size indicated in this table will deliver at least three or more times the number of INOS containers when using the Intel® Xeon® Gold 6338N.

6. Intel® AES-NI & Intel® Trusted Execution Technology: INOS SW runs on sensitive client data, and this capability will save data from any corruption and violation trials.

Conclusions & next steps

Having successfully completed this first assessment with the Intel Lab, the Digis Squared team are confident in the deployment of INOS as a cloud-based solution utilising Intel® Xeon® Gold processors, delivering optimised performance and enhanced speeds.

Yasser concluded, “Further work is planned with the Intel Lab team assessing other enhanced processors and measuring performance enhancement, and then, mutual testing with Open RAN market leaders!”

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

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Digis Squared, independent telecoms expertise.

AI enhancement of capacity management in mobile networks

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The optimisation of capacity management in mobile networks is vital: too little capacity constraints revenue opportunities and impacts customer experience, but idle capacity risks high opex and under-performing investment in assets. Capacity management has always used mathematical modelling techniques to attempt to find the sweet spot, and optimise opportunities and costs. In the past, such predictions were based on historical data, but now AI enhancement of capacity management changes that. The deployment of network virtualization, 5G and network slicing requires the use of cognitive planning; it is vital that capacity planning models are able to assess a step-change in the volume of data points in real-time or near-real-time.

RAN Automation Architect and Data Scientist at Digis Squared, Obeid Allah Ali, describes how AI, automation and advanced analytics are being deployed to gain even greater network capacity planning efficiencies.

What exactly is machine learning, and why is it important?

Machine Learning (ML) is an application of artificial intelligence (AI) that enables computer programs to learn and improve over time because of their interactions with data.

It automates analytics by making predictions using algorithms that learn repeatedly.

Its easy self-learning technique, rather than rule-based programming, has found widespread use in a variety of contexts.

So, whether it’s making life easier with navigation advice based on predicted traffic behaviour, assessing large amounts of medical data to identify new patterns and links, or warning you about market volatility so you can adjust financial decisions, AI and ML technology has permeated many aspects of our daily lives.

The power of prediction machines

In simplified terms, prediction is the process of filling in the missing information. It takes the information you have, often called ‘data,’ and uses it to generate information you don’t have. Most machine learning algorithms are mathematical models that predict outcomes.

How will machine learning impact businesses?

There are two major ways that forecasts will alter the way businesses operate.

  1. At low levels, a prediction machine can relieve humans of predictive activities, resulting in cost savings, and for example removing emotional bias.
  2. A prediction machine could become so accurate and dependable that it alters how a company operates.

How big is the growth in mobile connectivity?

Above: from GSMA “The State of Mobile Internet Connectivity Report 2021” [3], their most recent report

Some further statistics on the growth in mobile data, from the same GSMA report [3],

  • global data per user reaching more than 6 GB per month – double the data usage for 2018
  • 94% of the world’s population covered by mobile broadband network
  • By the end of 2020, 51% of the world’s population – just over 4 billion people – were using mobile internet, an increase of 225 million since the end of 2019

And from [4] GSMA Mobile Economy 2021 report,

  • By the end of 2025, 5G will account for just over a fifth of total mobile connections.

Capacity and performance of mobile networks

The rapid growth of mobile traffic places enormous strain on mobile networks’ ability to provide the necessary capacity and performance.

To meet demand, communications services providers (CSPs), mobile network operators and their suppliers need a range of options, including more spectrum, new technology, small cells, and traffic offloading to alternate access networks.

To meet commercial business objectives, mobile network operators are under pressure to maximize the utilization of existing resources while avoiding capacity bottlenecks that reduce revenues and negatively influence end-user experience.

Additionally, network operators have to assess risk, contractual SLAs (especially in the context of MVNOS who utilise their network, and corporate contracts), the total cost of ownership, and the impact on customer experience, perception and brand.

Radio Access Network costs are estimated to be 20% of the opex cost of running a network [1]. And the impact of opex on network quality correlates strongly with increased ARPU and reduced churn; when network quality is highest, service providers benefit from a higher average ARPU (+31 %) and lower average churn (-27%) [2].

Finding the perfect balance of capacity, quality, efficiency and cost – not too much, not too little – is complex and dynamic.

Capacity forecasting for mobile networks

The Digis Squared team have developed machine learning algorithms and decoders that can, based on network activity, decode how User Traffic Profiles are changing. With the deployment of 5G and network slicing techniques, modelling network usage patterns and customer behaviour and predicting future demand becomes immediately far more complex – the only way to successfully model this will be with AI.

Detecting a problem

We detect anomalies in cells in the existing network, plus highly utilized cells, using machine learning and a design approach algorithm based on several reported KPIs. We use this information to distinguish what requires immediate attention from what should be monitored for proactive action. Using multivariable modeling techniques, that is, assessing multiple KPIs across each cell, enables us to have a highly nuanced model, optimising all available capacity.

Forecasting

Operators must be able to estimate the required traffic capacity for their mobile networks in this competitive climate to invest in extensions when they are truly needed, and deploy the most cost-effective solution, while maximizing investment and maintaining good network quality.
In this phase of the development of the model, we will discover future troublesome cells to guide our approach and actions using predictive models.

AI enhancement of capacity management: what’s next?

Today, we use an open-loop control system to apply our AI methods. However, as predictive model accuracy improves, we anticipate transitioning to a fully automated Self-Organized Network (SON) – enabling closed-loop network management with self-planning, self-configuration, self-optimization, and self-healing – system in the near future.

In conversation with Obeid Allah Ali, RAN Automation Architect and Data Scientist at Digis Squared.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

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Digis Squared, independent telecoms expertise.

References

  1. Open RAN might not save you much after all
  2. Why network quality is more important than ever
  3. GSMA “The State of Mobile Internet Connectivity Report 2021”
  4. GSMA “Mobile Economy 2021” report

AI-native network slicing for 5G networks

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AI-native network slicing for 5G networks. Successful 5G deployments rely on the use and integration of many other new technologies. In this blog post, Tameem Sheble RAN data scientist at Digis Squared, and AI for 5G researcher, takes us on a deep dive through the close integration of 5G with AI, and how network slicing is vital to deliver the 5G vision.

The view from 2015: the 5G vision

New telecom technologies take years to be discussed, agreed and defined in standards. Previous wireless technologies have been designed and architected for one major use case: enabling mobile broadband.

During the development phase of 5G standards, by means of reaching consensus and aligning expectations, the International Telecoms Union (ITU) defined their framework and overall objectives of the future out to 2020 and beyond, in a vision document. As part of this workaround 5G architecture, they considered three distinctive, cutting-edge service verticals – enhanced mobile broadband, massive machine-type communications and ultra-reliable and low-latency communications – and their usage scenarios and opportunities for communications service providers (CSPs).

Above: The original vision for 5G. Diagram from “IMT Vision for 2020 and beyond”, published 2015 [1]

Network Slicing (NS) is one of the 5G key enablers. It’s a technique that CSPs can use to satisfy the different needs and demands of the 5G heterogeneous verticals, as illustrated below, using the same physical network infrastructure.

AI-native network slicing for 5G networks

Network slicing enables the virtual and independent logical separation of physical networks. It’s a technique used to unlock the value of 5G networks, by opening the possibilities for customer-centric services based on the demand on the network while managing cost and complexity. Consequently, vendors and standardization communities consider 5G NS a key paradigm for 5G and beyond mobile network generations.

Although the 5G NS process brings flexibility, it also increases the complexity of network management. The introduction of AI into the 5G architecture, AI-native, is motivated by the vast amount of unexploited data and the inherent complexity and diversity that requires AI to be deployed as an integral part of the overall system design. Although the rising temptation is to rely on AI as a pillar for managing 5G network complexity, in practical terms, AI and 5G are indivisible. They tend to converge from an application perspective, and they become two halves of a whole. AI’s value relies on 5G; for example; critical data-driven decisions need to be communicated with ultra-low latency and high reliability.

AI as a potential solution to network slicing

Let’s turn now to addressing AI in a nutshell for the management of the complex sliced 5G network, a complexity that relates to decision-making towards efficient, dynamic management of resources in real-time. CSPs need to leverage the use of the vast volume of data flowing through the network in a proactive way, by forecasting and exploiting the future system behaviour.

The management lifecycle of a network slice consists of four main phases,

  1. Preparation
  2. Instantiation
  3. Operation
  4. Decommissioning.

Many researchers have proposed AI solutions that underline the first 3 phases, as the decommissioning phase doesn’t involve management decisions. Admission control and network resources orchestration are some of the key slice management functions that need to make slice-level decisions to meet their requirements, while simultaneously maximizing the overall system performance.

Looking at this in more detail, this is achieved by controlling a massive number of parameters as a result of uncovering complex multivariate relationships that are related to each other in time, geolocation, etc. Proposing an AI solution must be done case by case depending on problem formulation and framing, algorithmic requirements, the scarcity and type of data and the operational time dynamics.

AI for network slices admission control (Phase 1)

Admission control – during the slice preparation phase – is a very critical decision-making control mechanism, it ensures that the requirements of the admitted slices are satisfied. During this control mechanism, a trade-off between resources sharing and KPIs fulfilment needs to be tackled. The decision on how many network slices run simultaneously, and how to share the network infrastructure between those slices, has an impact on the revenues of the CSPs.

The trade-off is further complicated by variables that alter over time, which makes the optimization of revenue based on admitted slices a difficult task. This is where Deep Reinforcement Learning (DRL) approach comes into the picture.

In a nutshell, the DRL algorithm has to learn the arrival pattern of network slices and make, for example, revenue-maximizing decisions based on the current system utilization and the anticipated long-term revenue evolution. Once a network slice is requested, and based on the system current utilization, two separate neural networks are in charge of scoring the two actions (i.e., accepting or rejecting the request), where each score represents the revenue associated with each action. Based on the difference in scores, the action corresponding to the higher revenue is selected; the algorithm interacts with the system and evaluates the accuracy of the forecasted revenue through a loss function. This value is then feedback to the corresponding neural network to perform weight update, so that the algorithm starts converging to a global maximum and performs better in the subsequent request iterations.

AI for network resources orchestration and re-orchestration (Phase 2 and 3)

After the successful admission, slices must be allocated sufficient resources in such a way that the available capacity is used in the most efficient way that minimizes the operational expenses (OPEX). The trade-off here is between under-provisioning that leads to Service Level Agreement
(SLA) violation, and over-dimensioning thus wasting resources.

CSPs need to be proactive by forecasting, at a slice level, the future capacity needed, based on previous traffic demand, and consequently timely reallocate resources when and where needed. This is where the Convolutional Neural Network (CNN) architecture comes into the picture for time-series forecasting.

Legacy state-of-the-art traffic time-series forecasting models focus on forecasting the future demand that minimizes some symmetric loss (e.g., mean absolute error), that treats both under- and over-prediction equally. But this type of legacy approach doesn’t consider the risk of under-provisioning and SLA violation – it is useless for 5G deployment!

Researchers argue that a practical AI-native resource orchestration solution has to forecast the minimum provisioned capacity that prevents SLA violation. The balance between over-dimensioning and under-provisioning is therefore controlled by the CSPs, by introducing a customized loss function that overcomes the drawbacks of the “vanilla symmetric losses”.

The AI literature proposes the use of 3-dimensional CNN architecture over the recurrent neural network (RNN) architecture – which is considered the state-of-the-art algorithm for forecasting time-series data –  in order to exploit and uncover spatial and temporal traffic relationships.

The future for AI in 5G and beyond

Whilst general AI limitations are now well known – trustworthiness, generalization and interpretability – exploiting AI to assess and manage complex decisions is vital for the smooth operation of 5G networks. And as network technologies continue to grow in complexity and capability, AI will clearly be necessary as a pillar technology for future-generation zero-touch mobile networks

In conversation with Tameem Sheble RAN data scientist at Digis Squared, and AI for 5G researcher.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

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Digis Squared, independent telecoms expertise.

References

  1. IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond (2015) – Figure 2, page 12, Usage scenarios of IMT for 2020 and beyond

In-building coverage testing without an engineer on-site, with INOS

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With an ever-growing volume of wireless network traffic produced inside buildings, network design and performance must be evaluated from within buildings. In this blog post, we assess the growing need for indoor coverage and the impact of the pandemic, plus Digis Squared RAN and Software Solution Architect and Trainer, Amr Ashraf, describes how in-building coverage testing without an engineer on-site can be undertaken, with INOS.

The ever-growing importance of in-building coverage

Often quoted research (1) estimated that “approximately 80 percent of wireless data traffic originates or terminates within a building”. Anecdotally, that figure is far higher now. Lockdowns and work-at-home mandates of the Covid-19 pandemic, plus the growing need to digitally maintain contact with friends and family are sure to have driven this even higher.

The pandemic has generated, and increased, specific needs for wireless connectivity indoors,

• Switch to working at home
• Increase in voice traffic and video conferencing/communication, gaming and streaming traffic as we stay connected online at home to friends and family
• Apps handling proximity detection/tracking and alerts about infected contacts
• Tele-medicine: urgent care assessments and consultations, updating families unable to visit, remote assessments and advice, maintaining safe care-homes for the elderly and hospice patients

Even aside from the pandemic, the explosion in social media and mobile-centric content generation and consumption has dramatically increased the volume of mobile data consumed indoors.

But if indoor coverage is poor, then this impacts both operator revenue, and, perhaps more critically, brand loyalty and churn, as the need to connect now, indoors is far higher than any remaining loyalty consumers (and businesses) have for an operator’s brand.

Testing wireless connectivity inside buildings

Digis Squared RAN and Software Solution Architect and Trainer Amr Ashraf shares insights into the challenges and solutions for testing indoor coverage.

“Indoor network testing presents its own set of challenges, not encountered when undertaking traditional outdoor drive-testing. These indoor challenges include everything from gaining physical access to the site, to collecting as much relevant benchmarking data as possible in a single pass, and determining whether solutions provide data uploads to the cloud and data processing in a timeframe that enables a technician to test and troubleshoot network issues in one visit – if there is poor network coverage indoors, this may impact the speed at which we can assess the results!”

“Indoor testing today utilises smartphone and tablet applications, with all equipment packed discretely into a backpack-based test solution for indoor network testing. This approach has led to the number of walk-testing options for interior settings significantly expanding in recent years. Then, with detailed plans or architecture drawings of the building, and an efficient walking route planned out, a team member can be tasked with wearing the back-pack, starting the app, and walking through the route.”

“As mobile network operators and communications service providers have concentrated more and more on in-building coverage, they often encounter a problem: they are unable to gather all the measurements they need in a single test walk.”

“Critically, it’s no longer necessary for the person walking the route inside the building to be an engineer. The technical assessment can be undertaken by skilled staff, remotely, ensuring your scarce engineering resource can be deployed efficiently across many projects. When an issue is detected during the building walk-through, the network can be optimised remotely – and because the INOS testing and analysis takes just 15 minutes from receipt of data, our aim is to ensure that we can re-test and re-walk the improved area as part of a single visit to the building.”

“One of our clients described testing a distributed antenna system at a major convention centre that served four wireless operators using three different wireless technologies across multiple channels, for a total of about 20 different operator/technology/band combinations, each of which required a separate measurement. A complex configuration, but one which is quite common in large business-focused buildings.”

“The indoor network testing for this project was carried out with INOS using the Digis Squared proprietary backpack-based In-Building Test Suite. In contrast to user-equipment-based backpack testing systems, which are typically restricted by the number of devices and technologies that can be tested concurrently, the INOS solution depends on a scanning receiver intended for multi-technology networks. That is to say, we are not constrained, there is no technical limit on the number of devices we can use in the testing.”

Undertaking an in-building survey

“The INOS backpack is a multi-technology integrated solution for testing and measuring multi-device mobile networks. Whether it’s for conducting an indoor or outdoor walk or cycle test, or an outdoor drive test, the INOS backpack offers a small design for portability and simple movement. Data interaction is accomplished by using a WiFi hotspot to link an Android tablet (as a controller unit) to test terminals. A powerful solution for portable multi-network benchmarking, supporting up to 20 test terminals and a scanner for testing and measuring simultaneously.”

“The measurements are transferred to the cloud for additional data management and processing, and the testing is undertaken according to the test plans given by the controller unit.”

“We use an Android tablet to operate all of the testing equipment in the backpack, connected via Wi-Fi to the test phones, which are also integrated into the backpack. This configuration gives the technician complete control over the devices, enabling them to add pinpoints throughout the in-building walk as data is collected, or repeat sections immediately after dynamic network optimisations are implemented.”

Part of the INOS interface showing the controlling tablet view, with information about the connected testing devices and their status.

Case study

“Recently, a global Tier One mobile operator used the Digis Squared INOS backpack testing technology to investigate networks in Cairo. They wanted to undertake benchmarking on their own network, as well as those of their main rivals, both inside buildings and outside. Data speeds, latency, and web browsing durations were among the main performance parameters they tested with INOS, as were dropped calls and RSSI signal levels. Once captured, the INOS data collected was sent over the air to the INOS cloud-based platform for immediate automated analysis and presentation via an analytics dashboard.”

INOS data captured during in-building testing inside the “Mall of Arabia”, in Cairo, Egypt

INOS advantage compared with traditional approaches

“One of the primary advantages INOS delivers is our very quick analysis and reporting capability. After just a few minutes of testing, we can practically immediately provide a comprehensive report with all KPIs.”

“The vast majority of network coverage-related complaints occur indoors, traditionally necessitating an engineer to visit the customer’s house or office to undertake a network evaluation – this legacy approach results in high operational costs, and scheduling delays in identifying the issue.”

“Let’s compare that with the INOS solution. Anyone can be tasked with capturing data with INOS, no technical knowledge is needed to carry the backpack around the building or location of interest. It is not necessary to divert a skilled engineer out in to the field to capture data – on some projects we’ve tasked Uber drivers with taking an INOS bag around a pre-defined route, and returning it to us, or asked a member of the admin team to cycle a route with the INOS backpack. The INOS system can even be utilised to submit a self-service complaint to skilled RF optimization specialists in the office, who can then undertake an initial assessment remotely using the INOS kit controller and web application. And of course, as only one person is needed to take the bag in a car, or walk it around a building, the solution is also Covid-19 safe.”

“INOS also enables operators and suppliers to capture data in the field remotely, analyse the data, determine which issues can be solved remotely, and then efficiently schedule and resolve problems which can only be addressed in the field .”

“If you want to know more, we’re always happy to chat through what we can do to help you. Meet us at MWC22 or let’s fix up a call online.”

In conversation with Amr Ashraf, Digis Squared RAN and Software Solution Architect.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

Key Advantages of INOS

  • Tablet: From the Android picture gallery, users can quickly import tiny to huge floor plans (of any form of structure).
  • Floor plans and data are kept in the cloud and may be shared with co-workers.
  • Ease of use, testing, and interior navigation can all be undertaken by non-technical personnel.
  • In real time, test data is uploaded to the INOS Cloud server.
  • Post-analysis: results can be mapped onto indoor floor layout, with a web-based dashboard.
  • All-in-one mobile solution with device, network, and service benchmarking capabilities.
  • From the standpoint of subscribers, it provides extensive network performance statistics.
  • INOS is used as the test device, allowing for a single investment to be used for multiple purposes.
  • Test procedures, data processing and analysis can be fully automated, resulting in increased overall efficiency, and optimised consistency.

Discover more

Digis Squared, independent telecoms expertise.

Image credits

  • Digis Squared social media and blog banner image: Sung Jin Cho
  • With thanks to Digis Squared’s Ziad Mohamed
  • All INOS images: (c) Digis Squared

References

  1. ABI Research 2016, Commscope/Coleman Parkes 2015 research

Cognitive solutions for telecom operations

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Digis Squared Chief Technology Officer, Abdelrahman Fady, shares insights into Digis Squared’s approach to cognitive solutions and AI.

“Today, there are three distinct and significant challenges that Mobile Operators face,

  • 5G and new technologies are adding extra dimensions of complexity to the networks
  • Mature markets, ever-increasing customer expectations, and higher standards to reach for customer satisfaction
  • Revenues shrinking and increased budget pressures.”

“For sure, you can find an opportunity in every challenge,” shares Abdelrahman, “and here the opportunities we found within this complexity, pressure and maturity is the existence of massive amounts of data and very strong computational power. So let’s see how we can tackle these challenges using the created opportunities. This article digs into some answers!”

What is cognitive technology?

“Yes, it is software-based technology built on the 3Vs – volume, variety, velocity. Characteristics of big data lakes generated from networks, deployed over the strong computational power provided to us by new technologies, in combination with ML advanced modelling that fits in with SMEs unique logic.”

“Cognitive technologies refer to a multiple set of techniques, tools and platforms that enable the implementation of intelligent agents.”

Intelligent agent tasks can be considered as,

  1. Sense
  2. Think: Previous knowledge + known data
  3. Act

Intelligent agent thinking stakes: how cognitive agents work with ML & MR

“Cognitive computing represents self-learning systems that utilize machine learning, ML, and machine reasoning, MR, models to mimic the way brain works,” explains Abdelrahman.

The characteristics of cognitive computing include that they are,

  • Adaptive: cognitive software mimics the ability of human logic and brains to learn from and adapt to its surroundings
  • Interactive: cognitive solutions interact with all elements in the system (processors, devices, clouds and users)
  • Iterative: cognitive software always remembers previous interactions in a process
  • Stateful: cognitive solutions return suitable information
  • Contextual: cognitive software is capable of identifying contextual elements such as syntax, time, location, users, profiles etc

Cognitive benefits

“Cognitive solutions nowadays are in the circle of focus of all mobile operators. Applying them in technical operations as well as commercial operations are likely to bring a lot of benefits to operators,” says  Abdelrahman.

  • CAPEX rationalization: “Decisions about where to add new sites, layers, technologies, where and when to undertake network expansion should be taken based on many factors. ROI is part of this decision-making process, along with many other technical and commercial aspects, including the network growth and consumer behaviour changes, commercial positioning in the market, the general economic climate. Cognitive software like Smart Planning (Smart Capex) software ensure that proper investment and budgeting decisions are based on the complex interaction of such a diverse range of factors.”
  • Operational efficiency “Optimizing operations activities and resources are vital, and the hottest active topic these days due to the impact of COVID-19 on the overall telecom ecosystem. Automation has been used for ten years or more in the telecoms sector, however continuing to reach high efficiency targets needs more than just automation. It needs a combination of automation, AI, Big Data Analytics and human brain emulations, and this can only be achieved by deploying cognitive solution in operations.”
  • Superior network and customer experience “Smart Optimization for newly deployed sites, sectors and technologies are very important to enhance customer experience, and must work very swiftly to have impact. Additionally, network KPI enhancement plus handling customers pain-points before complaints arise or impact network churn KPIs are vital. It’s very important that all of these elements should be automated and continuously updated. To achieve that you must adopt smart cognitive solutions for network optimization.”
  • Fast time to market “Analysing consumer behaviour and response to what is offered by operators, as well as the impact of broader economic changes, help in the design optimisation of operators’ products and services. Because of the complexity of these inputs, the only way to assess the users and market needs is adopting cognitive technology in commercial analysis of client behaviour and product usage.”
  • Speedy mean time to resolve “Currently, mobile networks are very mature and very complex. In general, competitors are focused on customer centricity. Actually, this customer centricity couldn’t be in place without very accurate and decisive solutions that help us to identify and resolve network and customers’ technical and commercial issues and pain-points very quickly. This is one for the early targets achieved by the application of cognitive solutions and software.”

Cognitive technology limitations

“Having described and enthused about the benefits, lets provide some balance, and consider the limitations,” says  Abdelrahman.

  • Handling un-expected risks and abrupt changes are the most serious challenges that face cognitive solutions. Due to slow response times to this type of change, cognitive solutions risk not being very accurate and speedy. Continuous development and training for adopted models in cognitive solutions is the only way to mitigate these challenges.
  • Data bias: as with any AI system, and mathematical model, bias is always dangerous. To mitigate this requires diversified data sources.
  • Decision accuracy is another challenge here which arises from the risk and possibility of mimicking the human brains of inexperienced team members. This risk may be mitigated easily by adopting a check-points technique during the solution design phase.
  • Explain-ability & repeatability: as with any AI system, it is vital that developers are able to explain how the cognitive system arrived at the answer it did. Decision tree mapping is a vital part of this process, as is the ability to explain and demonstrate why variability or repeatability does / not occur.
  • Data protection, data privacy and security are very important legal, regulatory and ethical factors, especially when you are dealing in your solution with personal data usage. Governments, regulators and authorities are putting a lot of effort into protecting consumer data, and customers are increasingly aware and vocal on the issue. One of the techniques which is often implemented to mitigate this risk is to mask any personal info with code like mapping.

Digis Squared & cognitive solutions

“Digis Squared has a set of cognitive solutions, and extensive experience in this domain with multiple operators. Our solutions are already deployed and in action helping telecom operators and communications service providers in different regions to enhance their operational limits. If this is something you would like to know more about, I am always happy to discuss more with clients, get in touch,” shared  Abdelrahman.

Digis Squared cognitive operations

Our live cognitive solutions deployed today include,

  • Drone site audit
  • Smart CAPEX
  • Smart optimization.

“In an upcoming blog I’ll share more about a future vision for cognitive operations, and moving towards zero-touch network operations, full automation for FCAPS model.”

In conversation with Abdelrahman Fady, Digis Squared Chief Technology Officer.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

Digis Squared, independent telecoms expertise.

Image credits

  • Digis Squared social media and blog banner image: NASA

Digis Squared Open RAN projects and capabilities

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Digis Squared Open RAN projects and capabilities  |  Mohamed Hamdy shares details of Digis Squared’s Open RAN capabilities and describes the types of projects the team are currently working on.

This is the second in a series of blogs focussing on Open RAN, where Mohamed Hamdy, Chief Commercial Officer at Digis Squared, and AbdelRahman Fady, CTO, share their insights.

“The Digis Squared team believe that 100% of mobile operators and CSPs will move to Open RAN models sooner or later. Each MNO will deploy Open RAN according to their strategy, either in rural areas or in urban areas, and that’s why we’re giving strategic and operational focus to this.”

Mohamed Hamdy, CCO at Digis Squared

Digis Squared’s Open RAN expertise, solutions & capabilities

Mohamed, what insights can you share with us on the Open RAN work being undertaken at Digis Squared?

“The Digis Squared team believe that 100% of mobile operators and CSPs will move to Open RAN models sooner or later. Each MNO will deploy Open RAN according to their strategy, either in rural areas or in urban areas, and that’s why we’re giving strategic and operational focus to this.

The Digis Squared team started very early to build their competencies, expertise, tools and portfolio for Open RAN, as well as building a dedicated service portfolio to help MNOs to adapt their network architecture to the Open RAN model. Today, we provide insight-led multi-system and multi-vendor expertise across the entire network lifecycle.”


Digis Squared OpenRAN expertise, solutions and capabilities

“Our work tries to address five key challenges we frequently see when working with clients,

  • Lack of confidence in OpenRAN solutions
  • Sub-optimal performance with a limited vendor feature set
  • Delayed operator & vendor deployment
  • Duplicated operator & vendor interoperability testing for HW & SW
  • Lack of SI expertise for successful deployment

To address these issues we,

  • Provide independent, interoperability and performance benchmarking, for example, by working with EANTC
  • Undertake advanced E2E troubleshooting
  • Deliver extensive system release validation
  • Provide direct access to Open RAN expertise and experience in design, integration and deployment

And thereby deliver,

  • Minimised deployment costs
  • Accelerated time to value
  • Richer vendor feature set through roadmap alignment
  • High performing Open RAN solutions
  • And, successful Open RAN deployments.”

In conversation with Mohamed Hamdy, Digis Squared Chief Commercial Officer.

How can Digis Squared help you with Open RAN?

The Digis Squared team are here to help, and can provide their experience, AI-led tools, and capabilities to help operators and CSPs with all aspects of Open RAN strategy, testing and deployment optimisation.

  • We provide the industry with a range of OpenRAN related services including integration, performance benchmarking and systemisation.
  • Collaborate with operators, vendors, system integrators and research institutes to promote and accelerate OpenRAN ecosystem development, focused on,
    • System Integration
    • Interoperability between vendor components
    • Release validation
    • End to end performance benchmarking
    • Trials and PoCs.
  • Showcase and promote OpenRAN within the industry (TIP, O-RAN, GSMA)
    • Capacity solutions, cost-effective rural coverage, 5G solutions.

This Digis Squared Open RAN blog reveals some of the capabilities we have, and if you or your team would like to discover more about our OpenRAN capability, or other elements of the work we do, please get in touch: use this link or email sales@DigisSquared.com .

Read AbdelRahman Fady’s blog, CTO insights into Open RAN features and vendor flexibility.

Keep up to speed with company updates, product launches and our quarterly newsletter, sign up here.

Digis Squared, independent telecoms expertise.

Abbreviations

  • CSP Communications Service Provider (ComSP)
  • SI System Integration

Image credit, Digis Squared social media and blog banner image: Charlotte Harrison

CTO insights into Open RAN features and vendor flexibility

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Open RAN  features and vendor flexibility | Digis Squared CTO AbdelRahman Fady shares his insights.

In this series of blogs focussing on Open RAN, AbdelRahman Fady, Digis Squared CTO, and Mohamed Hamdy, Chief Commercial Officer, share their insights.

In this first blog, AbdelRahman considers traditional, legacy RAN implementations, why Open RAN is needed, and the benefits and options it brings. He looks at technical features and vendor selection considerations, and how to balance features, flexibility and efficiency.

Open RAN features and vendor flexibility: a technical overview

AbdelRahman talks us through traditional legacy RAN implementations, and the new architectures and benefits of Open RAN.

“Mobile networks comprise two domains: the Radio Access Network (RAN), and the Core Network (Core).

  • RAN: the final link between the phone and network. Includes the antennas and towers we see on top of buildings, as well as base stations. The RAN base station digitises the signal to and from devices. One of the most expensive parts of the network.
  • Core: Provides access controls, authentications of users, routes calls, handles charging and billing, manages interconnect to other networks and internet. Ensures continuity of connection as a user moves and travels from one RAN tower to another.

Traditional, legacy RAN: hardware and software are very closely linked; selecting a vendor for the traditional RAN implementation guarantees performance, however it also constrains feature roadmap. A lack of interoperability means that there is little choice in where equipment is sourced, or the ability to influence innovation; however, identifying the vendor responsible for any fault resolution is simple.

In legacy RAN implementations each single site has its own hardware, however there is no concept of pooling sites’ baseband processing, and this leads to inefficient utilisation of hardware resources.

Legacy sites still need a sufficient space for all the equipment, plus an excellent and reliable power source, and these requirements and limitations have a big impact on the MNO or CSP’s OPEX.

The total cost of ownership (TCO) of Radio sites is still very high, and this impacts the scale and pace of CSPs expansion plans, especially in city outskirts and rural areas; any expansions is completely locked into the current vendor’s equipment.

The drive for new RAN architecture has been powered by better resources utilisation through pooling, and more powerful processing through centralisation. Additionally, the introduction of machine learning (ML) concepts in handling radio resources, as well as reducing sites TCO. The new RAN model should provide CSPs with implementations that need far less space and power, thereby significantly reducing the OPEX.

Before addressing the new RAN architectures, we will first consider the main RAN components we have and how can we split them.

RAN solutions typically have three key components,

  • Radio Unit (RU): radio frequency signals are transmitted, received, amplified and processed
  • Baseband processing: all the digital processing over the signals, along with all the interfaces needed to the transport network, and the CPU functions of the site. Today we can split this function into,
    • Distributed Unit (DU): handling all real time processing over the signal
    • Centralised Unit (CU): non-real-time processing over the signals plus the main computational function for the signal.

3GPP has defined models to split the functions between DU and CU, and provide the CSP/MNOs with a high degree of freedom to deploy the most suitable split model according to their network readiness. With new RAN architectures, away from legacy solutions, there are different implementation options based on the location of DU and CU,

  • Distributed Cloud RAN
    • DU: co-located with RU on the same site, where the remote Radio Unit (RRU) is connected to the DU through fronthaul interface (eCPRI)
    • CU: co-located near(er) the Core, and connected to DU through mid-haul transport network with specific transport network requirements, and connected to the central network (CN) through the backhaul
  • Dual split Cloud RAN
    • DU: is located away from RU, within Edge Cloud. More than cell site could be connected to the same DU, however, the fronthaul requirements should be achieved by the transport network.
    • CU: co-located near(er) the Core, and connected to DU through the mid-haul transport network, with specific transport network requirements, and connected to CN through the backhaul
  • Centralised RAN DU & CU:  centralised in the same location, near to the CN

The selection of the architecture to be deployed, and the functional split model should be carefully considered, with particular awareness of the transport network readiness and capabilities.

DU and CU concepts are introduced along with the concept of virtualisation; now the HW and SW are not locked to a specific vendor and from here we can jump to the ORAN concept.

Open RAN aims to ensure that the interfaces between these components are standardised, interoperable and open – expanding the ecosystem of solutions and vendors, driving speed and diversity of innovation and opening up greater flexibility in deployment.”

Benefits: Open RAN features and vendor flexibility

“Open RAN aims to deliver greater flexibility and vendor choice. When this is implemented as vRAN, the open and flexible architecture virtualizes network functions in software platforms based on general purpose processors.”

Together Open RAN as vRAN can deliver,

  • Cost savings: virtualised network, with containerised components – true scalability and cost management.
  • Sharing via network function virtualisation – one or more virtual machines run different software and processes, on standard high-volume servers, without the need for custom hardware appliances for each network function – enables multiple operators to securely run segregated networks, side by side on the same platform. In the future, this will also enable network sharing through software.
  • Vendor choice: contractual flexibility to balance features, cost, and adjust future decisions; opening up and standardising interfaces gives a greater choice of vendor solutions.
  • Third-party testing: plug-fests and independent testing will give MNOs and CSPs greater clarity on capability and interoperability, enable benchmarked KPIs, and test-labs will develop deep knowledge of quirks and capabilities of different systems.

Open RAN architecture

“The model above shows the new open interfaces available as part of Open RAN. These have been introduced between fully virtualised nodes with the newly standardised concept of RIC (RAN intelligent controller, with near RT RIC and non real time RIC options) for controlling the radio resources and features. They enable huge opportunities for new vendors to innovate new algorithms and features to enhance the overall performance of the new system supported with Machine Learning and Deep Learning algorithms.”

Challenges for the new RAN evolution

AbdelRahman, you have shared a lot of technical insights into the changes in RAN technology, and the benefits the new standards and architecture OpenRAN will bring. But let’s balance that out, it can’t all be good news!

AbdelRahman, what do you consider to be the three greatest challenges currently?

  1. “Performance: For sure, comparing the performance of very mature solutions from vendors who deployed very early, against the very latest ORAN vendors solution is not very fair! There is still a long way to go to reach good maturity for ORAN solutions
  2. Real interoperability: Actually, one of the big issues of the ORAN nowadays is the full interoperability between OS, SW, HW and orchestrators vendors. In reality, today, not all vendors are compatible for the time being, and that’s why, before deployment, CSPs still need to do IOT interoperability testing of the solution
  3. Infrastructure readiness: In ORAN the fronthaul interface is mostly conveying real time data and signalling. That’s why we need to adopt very strict performance requirements between sites and EDGE clouds or Central clouds according to the selected split options.”

In conversation with AbdelRahman Fady, Digis Squared CTO.

A whole new world of acronyms

Let’s answer some common queries!

Is cloud RAN the same as Open RAN? And what about vRAN?

  • Cloud RAN / C-RAN: centralised, consolidating the baseband functionality across a smaller number of sites in the telco’s network and cloud.
  • Virtualised, vRAN: more open and flexible architecture which virtualizes network functions in software platforms based on general purpose processors.
  • Open-RAN (notice the hyphen!): uses new open standards to replace legacy, proprietary interfaces between the baseband unit (BBU) at the foot of the cell tower and the remote radio unit (RU) at the top of the tower.

What is the difference between O-RAN, OpenRAN, Open-RAN and Open RAN?

  • O-RAN: an organisation, the O-RAN Alliance. Work to support open standards.
  • OpenRAN: a standard written by TIP, Telecom Infra Project.
  • Open-RAN (notice the hyphen!): uses new open standards to replace legacy, proprietary interfaces between the baseband unit (BBU) at the foot of the cell tower and the remote radio unit (RU) at the top of the tower.
  • Open RAN: industry-wide interface standards that enable RAN equipment and software from different vendors to communicate.

How can Digis Squared help you with Open RAN?

The Digis Squared team are here to help, and can provide their experience, AI-led tools, and capabilities to help operators and CSPs with all aspects of Open RAN strategy, testing and deployment optimisation.

  • We provide the industry with a range of OpenRAN related services including integration, performance benchmarking and systemisation.
  • Collaborate with operators, vendors, system integrators and research institutes to promote and accelerate OpenRAN ecosystem development, focused on,
    • System Integration
    • Interoperability between vendor components
    • Release validation
    • End to end performance benchmarking
    • Trials and PoCs.
  • Showcase and promote OpenRAN within the industry (TIP, O-RAN, GSMA)
    • Capacity solutions, cost-effective rural coverage, 5G solutions.

If you or your team would like to discover more about our OpenRAN capability, or other elements of the work we do, please get in touch: use this link or email sales@DigisSquared.com .

Read CCO Mohamed Hamdy’s blog, Digis Squared Open RAN projects and capabilities.

Keep up to speed with company updates, product launches and our quarterly newsletter, sign up here.

Digis Squared, independent telecoms expertise.

Sources

  1. Nokia
  2. Mavenir 1 and 2

Abbreviations

  • CN: Central Network
  • CSP: Communications Service Provider (ComSP)
  • CU: Centralised Unit
  • DL: Deep Learning (AI)
  • DU: Distributed Unit
  • eCPRI: enhanced Common Public Radio Interface
  • HW: hardware
  • ML: Machine Learning (AI)
  • NFV: Network Function Virtualization (=VNF, Virtualized Network Function)
  • PoC: Proof of Concept
  • RAN: Radio Access Network
  • RIC: RAN Intelligent Controller
  • RU: Radio Unit
  • RRU: Remote Radio Unit
  • SW: software
  • VNF: Virtualized Network Function (= NFV, Network Function Virtualization)

Image credit, Digis Squared social media and blog banner image: Andy Newton @bacchanalia, The Floating Harbour, Bristol dock, UK.